The goal of this research is to test the performance of an experimental model for automated prescreening for cervical cancer. The difficulties posed by artifacts and patient-to-patient variability in routine clinical material will be evaluated, and the reduction in the number of smears that would have to be visually examined by cytologists if a prescreening device based on these principles were built will be determined. The diagnostic decisions are made by a two-stage decision process: a cell detection module identifies objects as potentially abnormal cells; a high resolution module extracts detailed information and determines whether the object is an artifact or a cervical cell. If a cell, it is classified as normal or abnormal. The performance of the cell detection module (CDM) has been tested on additional slides from normal and malignant origin. We scanned 431 monolayer slides over 10% of their area. On each malignant case, visual diagnostic assessment had identified tumor cells and their locations were on the computer. A total of 290,000 cells were tested by the CDM. Among these there were about 5,500 marked tumor cells. Fifty percent of these were correctly detected by the CDM. A total of 36,000 CDM-flagged objects were relocated and visually identified. CDM scanning alone thus allowed the unequivocal identification of about 60% of all malignant cases as positive. About 20% of the normal cases were not correctly recognized as negative. High resolution analysis was required for approximately 3,000 detected objects, including about 10% marked tumor cells. Shape, texture and spectral features for artifact identification were identified and examined with respect to their discriminating power; training sets were assembled. The principal cause for false alarms were dark debris, immature metaplastic cells, clusters of darkly staining cells and densely stained endocervical cells.